Fast Robust Pattern Classification Algorithms for Real Time Neuro-Motor Prosthetic Applications

In this paper, we give a brief review of pattern classification algorithms based on discriminant analysis. We then apply these algorithms to classify movement direction based on multivariate local field potentials recorded from a microelectrode array in the primary motor cortex of a monkey performing a reaching task. We obtain prediction accuracies between 55% and 90% using different methods which are significantly above the chance level of 12.5%.

Prototype learning methods for online handwriting recognition

In this paper, we study different methods for prototype selection for recognizing handwritten characters of Tamil script. In the first method, cumulative pairwise- distances of the training samples of a given class are used to select prototypes. In the second method, cumulative distance to allographs of different orientation is used as a criterion to decide if the sample is representative of the group. The latter method is presumed to offset the possible orientation effect. This method still uses fixed number of prototypes for each of the classes. Finally, a prototype set growing algorithm is proposed, with a view to better model the differences in complexity of different character classes. The proposed algorithms are tested and compared for both writer independent and writer adaptation scenarios.

Determinant: Old algorithms, new insights (Extended Abstract)

In this paper we approach the problem of computing the characteristic polynomial of a matrix from the combinatorial viewpoint. We present several combinatorial characterizations of the coefficients of the characteristic polynomial, in terms of walks and closed walks of different kinds in the underlying graph. We develop algorithms based on these characterizations, and show that they tally with well-known algorithms arrived at independently from considerations in linear algebra.

Tamarind orchards: Agroforestry for dry lands

Agroforestry has a potential for sequestering as much carbon if not more than forests. Massive benefits can be channeled to small farmers and landless labourers through cultivation of Tamarind and other fast growing and fruit yielding trees. This paper describes a project started by small farmers and landless labourers in a semiarid areas of south India. The aim is to upgrade dryland holdings of the member families through economically sound dry land horticulture, community woodlots, and planting of fast growing species along orchard and field boundaries. The small farmers invest massive labour inputs and project gives economic benefits to change their land use practices and improve environmental quality. This paper describes the planning. processes of the project, hurdles in finding AIJ partners, current monitoring procedures and costs of C sequestration. This shows this project is economically viable on its own, but initially needed, and continues to need Carbon credit investment in order to spread rapidly across the geopolitical region covered by the organization. It argues that economic gains to small farmers and landless labourers are the most certain way of achieving massive biomass increase and soil carbon replenishment, and that multiple holistic benefits are achieved through this kind of project.

Synthesis of configurable architectures for DSP algorithms

ASICs offer the best realization of DSP algorithms in terms of performance, but the cost is prohibitive, especially when the volumes involved are low. However, if the architecture synthesis trajectory for such algorithms is such that the target architecture can be identified as an interconnection of elementary parameterized computational structures, then it is possible to attain a close match, both in terms of performance and power with respect to an ASIC, for any algorithmic parameters of the given algorithm. Such an architecture is weakly programmable (configurable) and can be viewed as an application specific instruction-set processor (ASIP). In this work, we present a methodology to synthesize ASIPs for DSP algorithms.

Performance evaluation of neural network algorithms for multisensor data fusion in an airborne track while scan radar

This paper deals with the solution to the problem of multisensor data fusion for a single target scenario as detected by an airborne track-while-scan radar. The details of a neural network implementation, various training algorithms based on standard backpropagation, and the results of training and testing the neural network are presented. The promising capabilities of RPROP algorithm for multisensor data fusion for various parameters are shown in comparison to other adaptive techniques

A unified view of the Apriori-based algorithms for frequent episode discovery

Frequent episode discovery framework is a popular framework in temporal data mining with many applications. Over the years, many different notions of frequencies of episodes have been proposed along with different algorithms for episode discovery. In this paper, we present a unified view of all the apriori-based discoverymethods for serial episodes under these different notions of frequencies. Specifically, we present a unified view of the various frequency counting algorithms. We propose a generic counting algorithm such that all current algorithms are special cases of it. This unified view allows one to gain insights into different frequencies, and we present quantitative relationships among different frequencies.Our unified view also helps in obtaining correctness proofs for various counting algorithms as we show here. It also aids in understanding and obtaining the anti-monotonicity properties satisfied by the various frequencies, the properties exploited by the candidate generation step of any apriori-based method. We also point out how our unified view of counting helps to consider generalization of the algorithm to count episodes with general partial orders.

Fast algorithms for frequent episode discovery in event sequences

In this paper we consider the process of discovering frequent episodes in event sequences. The most computationally intensive part of this process is that of counting the frequencies of a set of candidate episodes. We present two new frequency counting algorithms for speeding up this part. These, referred to as non-overlapping and non-inteleaved frequency counts, are based on directly counting suitable subsets of the occurrences of an episode. Hence they are different from the frequency counts of Mannila et al [1], where they count the number of windows in which the episode occurs. Our new frequency counts offer a speed-up factor of 7 or more on real and synthetic datasets. We also show how the new frequency counts can be used when the events in episodes have time-durations as well.

Review of correlations between SPT N and shear modulus: A new correlation applicable to any region

A low strain shear modulus plays a fundamental role in earthquake geotechnical engineering to estimate the ground response parameters for seismic microzonation. A large number of site response studies are being carried out using the standard penetration test (SPT) data, considering the existing correlation between SPT N values and shear modulus. The purpose of this paper is to review the available empirical correlations between shear modulus and SPT N values and to generate a new correlation by combining the new data obtained by the author and the old available data. The review shows that only few authors have used measured density and shear wave velocity to estimate shear modulus, which were related to the SPT N values. Others have assumed a constant density for all the shear wave velocities to estimate the shear modulus. Many authors used the SPT N values of less than 1 and more than 100 to generate the correlation by extrapolation or assumption, but practically these N values have limited applications, as measuring of the SPT N values of less than 1 is not possible and more than 100 is not carried out. Most of the existing correlations were developed based on the studies carried out in Japan, where N values are measured with a hammer energy of 78%, which may not be directly applicable for other regions because of the variation in SPT hammer energy. A new correlation has been generated using the measured values in Japan and in India by eliminating the assumed and extrapolated data. This correlation has higher regression coefficient and lower standard error. Finally modification factors are suggested for other regions, where the hammer energy is different from 78%. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.